{
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   "source": [
    "## k-Means\n",
    "\n",
    "### 1.方法简介\n",
    "\n",
    "* 聚类算法是典型的无监督学习，样本点不像knn，是不包含标签的\n",
    "* kmeans是一种典型的聚类算法，是用迭代的方法重复移动重心得到的，主要步骤分2步：\n",
    "  （1）将重心移动到成员的平均位置\n",
    "  （2）重新划分内部成员\n",
    "* K是表示类的数量；K-Means可以自动分配样本到不同的类，但是不能决定究竟要分几个类\n",
    "\n",
    "### 2.算法解析\n",
    "\n",
    "kmeans的成本函数公式如下：\n",
    "$$\n",
    "J = \\sum_{k=1}^{K} \\sum_{i \\in C_k} | x_i - u_k|^2\n",
    "$$\n",
    "其中第k类的重心$u_k$位置：\n",
    "$$\n",
    "u_k = \\frac{1}{|C_k|} \\sum_{i \\in C_k} x_i\n",
    "$$\n",
    "\n",
    "成本函数实际上反映了畸变程度之和，每个类的畸变程度就是该类所有点到重心距离平方和，所以越紧密成本函数的值就越小，反之则越大。\n",
    "\n",
    "在代码实现时主要分为以下几步：\n",
    "1. 首先随机选定一个重心位置\n",
    "2. 每次迭代的时候，每个点会被归类到重心离他近的类当中，然后重新确定类的重心\n",
    "3. 迭代停止的条件：达到最大迭代步数，或者两次迭代差值小于阈值，这个解是kmeans方法的最优解，但未必是全局最优结果，在后面会有具体呈现"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "109f7d66",
   "metadata": {},
   "source": [
    "### 3.代码实现过程\n",
    "\n",
    "我们使用的数据是没有任何分类标签的，但是从直观上我们可以把它分成三类，所以我们首先选取了三个起始重心点\n",
    "\n",
    "这里make_blobs是sklearn中用来产生聚类的函数，[放一个简介](https://zhuanlan.zhihu.com/p/355738590)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1dc99a15",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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      "[ 4.47727698e+00  4.27286282e+00 -6.46572884e-01  6.39829339e+00\n",
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      "  6.98650278e+00 -5.30501148e-01  3.66598246e-01  5.68189149e+00\n",
      "  9.75119733e-01  7.33126343e+00  6.01849135e+00  6.18177932e+00\n",
      "  5.55979045e+00 -1.59442766e+00  3.71969560e+00  5.57249123e-02\n",
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      "  5.93567839e+00  3.88942415e+00  2.89774857e-01  3.38496407e-01\n",
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      "  0.11732738 -0.59157139  6.05315285 -1.29507877  0.51443883 -1.46351495\n",
      "  0.18463386 -0.79689526 -0.75373616  4.29987919  5.42545756 -1.40746377\n",
      "  1.87617084 -1.32045661  5.45675322  0.08187414  1.23781631  0.15372511\n",
      " -0.2257763  -0.97876372 -0.50694318  6.18839327  0.26105527 -0.97755524\n",
      "  3.87094823  0.75193303  4.43753322  0.30729952  5.70030988  5.20768769\n",
      "  5.71299843 -0.50347565 -1.9520878   4.47713997  4.22218331  1.35387237\n",
      "  0.17457781  0.82718325 -0.14436041  0.29698467 -0.23681861  0.41278093\n",
      " -0.71984421  5.6815007   0.66213067  7.01020454  0.95514232  0.12922118\n",
      " -1.95967012 -0.48712538 -1.02438764  5.50727403  1.05380205  5.97255445\n",
      "  0.38531738  1.54993441  0.62284993 -0.2209696   6.45338448  0.2322537\n",
      "  4.26906996  0.75138712  0.11092259  1.47535622  1.45353408 -0.32138584\n",
      " -0.05429487  2.57612067  2.19045563 -0.52575502  3.51443963  2.72016917\n",
      " -0.32206152  0.40405086 -1.2378155   5.64870989  0.47146836  0.34644821\n",
      "  0.21397991 -1.43586215 -0.38508228  5.39913611 -1.07774478  0.07409478\n",
      "  0.22388402  0.15039379 -1.10652591 -0.81581028  0.54256004  0.81350964\n",
      "  5.45918008 -0.70766947  6.0536418   0.75750771 -0.47765745 -0.56018104\n",
      "  5.44770856  0.0125924   4.34737602 -0.29900735 -0.77300978  1.66902153\n",
      "  0.71754226  0.91786195  5.02688584 -2.03923218  0.20346364  1.53273891\n",
      "  2.92660977  5.74625357 -1.12464209 -0.28865864  0.59515703  6.18901653\n",
      "  0.88163976 -0.46917565  5.38240975  4.46913123  0.10643023 -0.35151348\n",
      "  7.63238206  4.1053927   5.98269098  4.31801575  4.30809193  0.75896922\n",
      "  4.78601116  1.09877685  5.47141556  0.32875111  0.85243333  4.31894834\n",
      "  0.62834551  6.44011722 -0.21344715  0.65139125 -0.11473644 -1.00808631\n",
      " -0.56629773 -0.51121568 -0.1382643   0.24368721  5.70900376 -0.15567724\n",
      " -1.24778318 -0.90756366  4.23220243 -1.66152006  5.67481949  0.65655361\n",
      " -0.57689187  0.63391902 -0.57690366 -1.4123037  -0.21910053  1.87679581\n",
      "  5.87736229  0.79166269  0.54336019 -1.03524232 -1.21418861  3.33903907\n",
      "  0.93128012  5.44426331 -0.62269952  1.12656503  5.27902153  0.77463405\n",
      "  4.43592137  0.03526355  5.02609105  5.24333945  1.52955032  5.3520554\n",
      "  3.5692249   0.69620636  5.37721188  1.52302986  1.50399299  6.57745328\n",
      " -0.5100164   7.16325472  1.11957491 -1.1429703  -0.56228753 -0.32602353\n",
      "  6.69645637 -1.05921352  0.17136828 -1.98756891 -1.22084365  1.36687427\n",
      "  4.44452288  0.04739867  0.40498171  1.47994414  0.49799829  3.72042303\n",
      "  5.49245126 -0.85608383  5.76041466  6.09150685  0.06856297 -0.79287283\n",
      "  6.75479418 -0.65160035 -1.38279973  3.85273149  0.85639879 -0.15993853\n",
      " -0.23458713  0.61037027  0.19686124  4.9039401   5.34758171 -0.0555477\n",
      "  0.76743473  0.24822059  4.52125138  1.05712223 -0.31026676  1.36863156\n",
      "  4.81509786  4.71889971 -0.54342477  3.66465564  5.60600995  1.27767682\n",
      "  0.71400049 -0.64511975  4.18306433  0.04698059  4.63716144 -0.05952536\n",
      " -1.16867804  3.28983161  1.34542005  3.5915387   1.56464366  0.62962884\n",
      "  1.75534084  4.53772471 -0.82068232  0.37730049 -1.12548905 -2.21113531\n",
      " -0.40122047  0.72576662  0.18645431 -0.03269475  5.61058575  0.23204994\n",
      "  4.90328689 -0.24903604 -0.07444592  0.8496021   0.86575519 -1.42225371\n",
      " -0.92216532 -1.76304016  3.72325142  5.21915033  0.34115197 -1.42474819\n",
      "  0.37569802  5.74326409 -0.76734756  0.33366211  1.03246526 -0.23413696\n",
      "  3.92991523  6.06548038 -0.3011037  -0.12578692  4.95228864  3.42977528\n",
      " -0.74848654  6.4437646   2.95826513 -1.91328024  4.29682357  1.90941664\n",
      " -1.86726519  6.11729583  1.89679298 -1.65485667  4.69045356 -1.00252936\n",
      "  0.05921843  0.04808495 -0.14237949  6.01437007  0.35711257  4.08134805\n",
      "  0.66967255  5.15030176  0.78182287  0.57059867  0.31424733  5.6206721\n",
      " -0.33450124  4.60666119 -0.42688107  5.18660912  4.94305438  1.49604431\n",
      "  4.42925371  3.5933389 ]\n"
     ]
    }
   ],
   "source": [
    "# This line configures matplotlib to show figures embedded in the notebook, \n",
    "# instead of opening a new window for each figure. More about that later. \n",
    "# If you are using an old version of IPython, try using '%pylab inline' instead.\n",
    "%matplotlib inline\n",
    "\n",
    "# import librarys\n",
    "import numpy as np\n",
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# 生成数据\n",
    "#centers表示生成数据的中心点，人为设置三类，所以我们取三个中心点\n",
    "centers = [(7, 0), (0, 0), (5, 5)]\n",
    "n_samples = 500\n",
    "\n",
    "#features表示维度，cluster_std标准差，random_state固定生成的随机数\n",
    "#为了直观的显示，这里的X表示点的位置坐标，y则是点的类别标签，但是y不会用在聚类算法中\n",
    "X, y = make_blobs(n_samples=n_samples, n_features=2, \n",
    "                  cluster_std=1.0, centers=centers, \n",
    "                  shuffle=True, random_state=42)\n",
    "print(X[:,0],\"\\n\",X[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "279f6710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出数据\n",
    "plt.figure(figsize=(15, 9))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f3196e",
   "metadata": {},
   "source": [
    "主程序的代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af942572",
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算两点之间平方和\n",
    "def calc_distance(v1, v2):\n",
    "    return np.sum(np.square(v1-v2))\n",
    "\n",
    "#随机选取聚类重心\n",
    "def rand_cluster_cents(X, k):#k是由用户定义的类的数目\n",
    "    \"\"\"初始化聚类中心：通过在区间范围随机产生的值作为新的中心点\"\"\"\n",
    "\n",
    "    # 样本数\n",
    "    m=np.shape(X)[0]\n",
    "    \n",
    "    # 生成随机下标列表，这里选取的是所有点中的k个点\n",
    "    dataIndex=list(range(m))\n",
    "    random.shuffle(dataIndex)\n",
    "    centroidsIndex = dataIndex[:k]\n",
    "    \n",
    "    # 返回随机的聚类中心\n",
    "    return X[centroidsIndex, :]\n",
    "def kmeans(X, k):\n",
    "    # 样本总数\n",
    "    m = np.shape(X)[0]\n",
    "    # 分配样本到最近的簇：存[簇序号,距离的平方] (m行 x 2 列)\n",
    "    clusterAssment = np.zeros((m, 2))\n",
    "\n",
    "    # step1: 通过随机产生的样本点初始化聚类中心\n",
    "    centroids = rand_cluster_cents(X, k)\n",
    "    print('The initial center=', centroids)\n",
    "\n",
    "    # 初始化迭代次数计数器\n",
    "    iterN = 0\n",
    "    \n",
    "    # 所有样本分配结果不再改变，迭代终止\n",
    "    while True:   \n",
    "        # 标志位，如果迭代前后样本分类发生变化值为True，否则为False\n",
    "        clusterChanged = False\n",
    "    \n",
    "        # step2:分配到最近的聚类中心对应的簇中\n",
    "        for i in range(m):\n",
    "            # 初始定义距离为无穷大\n",
    "            minDist = np.inf;\n",
    "            # 初始化索引值\n",
    "            minIndex = -1\n",
    "            # 计算每个样本与k个中心点距离\n",
    "            for j in range(k):\n",
    "                # 计算第i个样本到第j个中心点的距离\n",
    "                distJI = calc_distance(centroids[j, :], X[i, :])\n",
    "                # 判断距离是否为最小\n",
    "                if distJI < minDist:\n",
    "                    # 更新获取到最小距离\n",
    "                    minDist = distJI\n",
    "                    # 获取对应的簇序号\n",
    "                    minIndex = j\n",
    "            # 只要有一个样本上次分配结果跟本次不一样，标志位clusterChanged置True\n",
    "            #只要在小循环里更新了类别，则minIndex会从-1变成新的类别索引，否则仍为-1\n",
    "            if clusterAssment[i, 0] != minIndex:\n",
    "                clusterChanged = True\n",
    "            # 分配样本到最近的簇，并且存下当前与重心距离平方，方便更新\n",
    "            clusterAssment[i, :] = minIndex, minDist ** 2  \n",
    "            \n",
    "        iterN += 1\n",
    "        sse = sum(clusterAssment[:, 1])\n",
    "        print('the SSE of %d' % iterN + 'th iteration is %f' % sse)\n",
    "        \n",
    "        # step3:更新聚类中心\n",
    "        for cent in range(k):  # 样本分配结束后，重新计算聚类中心\n",
    "            # 获取该簇所有的样本点,nonzero[0]表示A == cent的元素所在的行，如果没有[0],列也会表示\n",
    "            ptsInClust = X[clusterAssment[:, 0] == cent, :]\n",
    "            # 更新聚类中心：axis=0沿列方向求均值。\n",
    "            centroids[cent, :] = np.mean(ptsInClust, axis=0)\n",
    "        \n",
    "        # 如果聚类重心没有发生改变，则退出迭代\n",
    "        if not clusterChanged:\n",
    "            break\n",
    "            \n",
    "    return centroids, clusterAssment#返回重心和聚类结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb6e38ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial center= [[ 4.69222177  5.21915033]\n",
      " [ 4.63638779  4.94305438]\n",
      " [ 6.29794691 -0.32766215]]\n",
      "the SSE of 1th iteration is 296758.581326\n",
      "the SSE of 2th iteration is 84540.782701\n",
      "the SSE of 3th iteration is 27557.219640\n",
      "the SSE of 4th iteration is 3552.332815\n",
      "the SSE of 5th iteration is 3502.239035\n"
     ]
    }
   ],
   "source": [
    "# 进行k-means聚类\n",
    "k = 3  # 用户定义聚类数\n",
    "mycentroids, clusterAssment = kmeans(X, k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b454a79b",
   "metadata": {},
   "source": [
    "可视化聚类的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e170e0f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def datashow(dataSet, k, centroids, clusterAssment):  # 二维空间显示聚类结果\n",
    "    from matplotlib import pyplot as plt\n",
    "    num, dim = np.shape(dataSet)  # 样本数num ,维数dim\n",
    "\n",
    "    if dim != 2:\n",
    "        print('sorry,the dimension of your dataset is not 2!')\n",
    "        return 1\n",
    "    marksamples = ['or', 'ob', 'og', 'ok', '^r', '^b', '<g']  # 样本图形标记\n",
    "    if k > len(marksamples):\n",
    "        print('sorry,your k is too large,please add length of the marksample!')\n",
    "        return 1\n",
    "        # 绘所有样本\n",
    "    for i in range(num):\n",
    "        markindex = int(clusterAssment[i, 0])  # 矩阵形式转为int值, 簇序号\n",
    "        # 特征维对应坐标轴x,y；样本图形标记及大小\n",
    "        plt.plot(dataSet[i, 0], dataSet[i, 1], marksamples[markindex], markersize=6)\n",
    "\n",
    "    # 绘中心点\n",
    "    markcentroids = ['o', '*', '^']  # 聚类中心图形标记\n",
    "    label = ['0', '1', '2']\n",
    "    c = ['yellow', 'pink', 'red']\n",
    "    for i in range(k):\n",
    "        plt.plot(centroids[i, 0], centroids[i, 1], markcentroids[i], markersize=15, label=label[i], c=c[i])\n",
    "        plt.legend(loc='upper left')  #图例\n",
    "    plt.xlabel('feature 1')\n",
    "    plt.ylabel('feature 2')\n",
    "\n",
    "    plt.title('k-means cluster result')  # 标题\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "# 画出实际图像\n",
    "def trgartshow(dataSet, k, labels):\n",
    "    from matplotlib import pyplot as plt\n",
    "\n",
    "    num, dim = np.shape(dataSet)\n",
    "    label = ['0', '1', '2']\n",
    "    marksamples = ['ob', 'or', 'og', 'ok', '^r', '^b', '<g']\n",
    "    # 通过循环的方式，完成分组散点图的绘制\n",
    "    for i in range(num):\n",
    "        plt.plot(dataSet[i, 0], dataSet[i, 1], marksamples[int(labels[i])], markersize=6)\n",
    "\n",
    "    \n",
    "    # 添加轴标签和标题\n",
    "    plt.xlabel('feature 1')\n",
    "    plt.ylabel('feature 2')\n",
    "    plt.title('true result')  # 标题\n",
    "\n",
    "    # 显示图形\n",
    "    plt.show()\n",
    "    # label=labels.iat[i,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88e58ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图显示\n",
    "datashow(X, k, mycentroids, clusterAssment)\n",
    "trgartshow(X, 3, y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
